Riffing on the GA4GH Beacon concept as a path towards semantic web services for biomedical knowledge.

1. KNOWLEDGE BEACONS
Semantic Web Services for distributed question answering

2. QUESTIONS
• Why do variants that cause sickle cell anemia protect against malaria?
• In people with variants found in any of the 22 known FA genes, is there increased
incidence of aplastic anemia (or other diseases)?
• What venomous species have resulted in drugs approved by the FDA?
• What cellular processes in which tissues are impacted in a patient-based EMR?
• Why does ingestion of GlcNAc ameliorate symptoms of ngly1 deficiency?

3. DISTRIBUTED KNOWLEDGE
MANAGEMENT
• Just for today, the answer is not “put it all in wikidata” (or Monarch)
• Assume that we have to dynamically access knowledge sources (KS) that we don’t
control.
• Example, genetic data

4. DISTRIBUTED GENETIC
KNOWLEDGE
• A beacon is web service that any institution can implement to share
genetic data. A beacon answers questions of the form "Do you have
information about the following mutation?" and responds with one of
"Yes" or "No", among potentially more information.
• Experiment to test willingness to share genetic data in the simplest of
all technical contexts
• Input: chr16:28883241 A>G
• output: Yes I have data about that SNP, or no I don’t

5. • 90 beacons online

6. STAGE SET FOR DISTRIBUTED
GENETIC KNOWLEDGE BASES
chr16:28883241 A>G

7. QUESTION
•Why do variants that cause sickle cell anemia
protect against malaria?

8. THE TRANSLATOR
chr 11:5227002 A>T
causes
Sickle cell
anemia
pmid:20552021
Malaria
protects against
pmid:17668374
?
• How are they
related?
• Why 1 cause and
1 protect?

9. TRANSLATOR
• “A key output of this effort is a “product” that represents a unified interface to the
multiple groups, methods and approaches.” NCATS
• We assume from the outset that the required information is going to come from
different places.
• The goal is to build a system to access that information smoothly
• Step 1: where is it and who is willing to share ? (beacon)

10. KNOWLEDGE
BEACONS
• A knowledge beacon is web service that any institution can
implement to share genetic data knowledge. A beacon answers
questions of the form "Do you have information about the
following mutation concept?" and responds with semantic
relationships.
• Input: DOID:10923 (Sickle cell anemia)
• output:
DOID:10923
autosomal recessive disease
HBB
cerebral hemorrhage
isa
genetic association
has Phenotype

11. IMPLEMENTATION
K BEACON NETWORK
API: same as single BEACON +
• filter by provider
• list providers
• merge responses
CLIENTS (human/machine)
BLACKBOARD
KB4
Jupyter.. etc.
Swagger (Smart) API
• Auto-generated service stubs
Basic methods:
• list concepts by keyword
• get concept by CURIE
• get exact matching concept
identifiers
• get statements (semantic
relationships) by concept id
• get evidence for statements by
statement id
KNOWLEDGE BEACONS
https://github.com/STARInformatics/translator-knowledge-beacon

12. ANSWER HOW ARE THEY RELATED?:
BY BEACON TRAVERSAL
chr 11:5227002 A>T cerebral hemorrhage
has Phenotype
has Phenotype
Monarch
has Phenotype
renal insufficiency
Monarch
has subclass
HPO
Cerebral
Malariahas subclass
DO
causes
Sickle cell
anemia
MyVariant
Malaria
protects against
Wikidata
Abnormal renal
physiology
has Phenotype
Monarch
Enacted in any client,
provenance, evidence tracked

13. THAT’S STEP 1
• We have knowledge coming in from multiple sources
• Integrated into a coherent framework
• Allowing connections to be formed and some questions to be answered

14. WAIT BUT WHY
• Sickle cell shows up with 187 phenotypes,
• cerebral malaria with 52,
• malaria with 163
• There are likely thousands of paths that connect them.
• Just from Monarch
Why do variants that cause sickle cell
anemia protect against malaria?

15. 57 YEARS FROM
OBSERVATION TO
EXPLANATION
(stops damage to brain,
does not influence
pathogen load)

16. ANSWERING
THE WHY
QUESTION
A Sickle cell
anemia
pathway
Hb
HBB gene
variants
HBB gene
variant
Heme
Nrf2,
HO-1
CO
Sickle red
blood cells
Hb
Malaria
infect red
blood cell
Heme
Cerebral
Malaria
cerebral hemorrhage
Dying from
Malaria
makes too
much
makes too
much
too much
causes
causes
causes
(Dying from
Sickle cell)
Ferreira (2011)
Blocks ability to
release Heme
Blocks ability to
release Heme

17. DATA CRUNCHING SERVICES
• pathfinding, ranking: assuming those connections exist, how to find them?
• set analysis – given groups of e.g. patients, infer characteristics like ‘decreased heme
production’
• patient data: get to data needed to do this
• relation inference: simple (ontological expansion), complex (anlytical pipelines:
homology, expression analysis etc.)
• more..

18. TRANSLATOR
• The major consideration for Translator queries should be the extent to which they
are integrative and translational. That is, we would like to work with queries that not
only utilize multiple domains of knowledge, but do so in a way that goes beyond
simply pulling from the first source, filtering by the second, filtering by the third, and
so on. It should utilize unique relationships between multiples data sources.

19. ““THOSE WHO CANNOT REMEMBER THE
PAST ARE CONDEMNED TO REPEAT IT.”
• SADI
• WSMO
• OWL-S
• BioMOBY
• SAWSDL
• SSWAP
• caBIO
• myGrid
• TAMBIS
George Santayana The Life of Reason, 1905

20. 2001

21. THANKS
• Chris Mungall
• Richard Bruskiewich
• API specification
• https://github.com/STARInformatics/translator-knowledge-beacon
• Early implementation over semmedDB (wikidata version)
• http://default-environment.kmmdmp4hsz.us-east-1.elasticbeanstalk.com/api/swagger-ui.html
• Greg Stupp
• Beginning implementation over wikidata in Garbanzo
• http://52.15.200.208:5000/#/translator

22. IMPORTANT DETAILS FOR
DISTRIBUTED SYSTEMS
• registries
• concept identity
• syntax
• relation semantics
• service stability
• provenance
• evidence
• credit

23. QUERY = SERVICE ORCHESTRATION
• Specifying goals and inference strategies